Method for designating local representative color value and auto-determining detection algorithm on color image

ABSTRACT

A method for designating a local representative color value and automatically determining image searching algorithm for a color based image searching system is disclosed. A method for designating a local representative color values generally includes the steps of dividing a color image area; and obtaining a color histogram for each block, obtaining a color group from hue histograms of each block, or obtaining a maximum color group value to designate the representative color value. Also, a method determining a search algorithm generally includes the steps of detecting the number of color blocks CB having color information from the blocked information of a reference image; comparing the number of the color blocks with a determined reference value and assigning different weights to at least two search algorithm according to the comparison results; and performing a comparison search of an image by the search algorithm based on the determined weight.

This application is a Divisional of application Ser. No. 09/239,527filed Jan. 29, 1999 now U.S. Pat. No. 6,445,818.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a color image processing method, andmore particularly to a method for designating a local representativecolor value and automatically determining an optimal image searchingalgorithm on a color image.

2. Discussion of Related Art

In the field of color image analysis and processing, extensiveresearches for a method to accurately search images are actively beingconducted. In response, commercial image searching apparatus and/orapplications program are being developed to meet the demand for animproved image searching.

An image searching system based on content of the image is generallydivided into a feature extraction module extracting characteristics ofthe images, an image searching module searching the images, and a moduleof image database.

To expedite the searching process, the characteristics of images areextracted as the images are input to the image database and theextracted characteristics are also stored in the image database.Subsequently, if a user requires an image search, a similarity betweenrespective images are determined utilizing the characteristicinformation of a reference image and target images stored in thedatabase. The target images are sorted according to the result of thecomparison.

The most important information for use in the content based imagesearching is the color information. Accordingly, an effectiveperformance of an image searching apparatus or applications programdepends greatly on an accurate method for extracting the colorinformation.

Generally, the number of different colors which can theoretically beexpressed by a computer has been steadily increasing. However, thenumber of colors which can be displayed by the computer is limited bythe available number of quantized colors.

In computers, a color is expressed utilizing the RGB color model basedupon the three primary colors of red R, green G, and blue B. However,the RGB space is hardware oriented and a limitation exists in expressingthe color changes such that the change can be visually sensed by thehuman eye. Thus, the hue, chroma, and brightness of the RGB space isoften converted into a user oriented HSV color model based upon a hue H,saturation S and value V, then converted back to the RGB space byquantization. The CIE standards may be utilized to convert from the RGBspace to the HSV color space.

Numerous amount of content based image searching apparatuses orapplications programs utilizing the HSV space have been already proposedin the related art.

The characteristic information used for image searching are extractedfrom every pixel of an image and are mapped to color indexes. The weightof each color index on the image may be represented by a colorhistogram. Thus, the color histogram is an important data, indicating acolor distribution of the image. Generally, there are two types of colorhistograms represented by n number of quantized colors, namely a globalcolor histogram and a local color histogram.

In the local color histogram, an image is divided into n number of gridsand a local histogram for each grid cell is determined. A representativeindex color of each local histogram is defined as a local representativecolor of the corresponding grid.

Thus, the global color histogram indicates a color distribution in anoverall image and represents the total distribution of colors forrespective pixels of the overall image, and the local color histogramindicates a distribution in a local grids and represents the totaldistribution of the color on a specific region of the image.

To build a color histogram, the RGB values in each pixel of an inputimage is converted a user oriented color space, and the converted RGBvalues are mapped to one of n number of colors according to itsquantized color area. Based upon the mapped color, the global colorhistogram and the local color histogram are constructed for all imagepixels. Subsequently, the histograms of a reference image and targetimages are compared, and arranged in the order of highest similarity.Such order becomes a sequential array of most similar images by thecolor information.

In the content based image searching system, accurate color values ofthe image must be obtained to perform an effective analysis.

FIGS. 1A through 1E show an example method for designating a localrepresentative color value on a color image. FIG. 1A shows an image tobe analyzed, FIG. 1B indicates a color histogram for an overall image,FIG. 1C shows the image by block or grid state, FIG. 1D sets forth alocal histogram for each grid; and FIG. 1E shows the designated localrepresentative color value for each grid.

Particularly, the color characteristic of the image is represented bythe global color histogram as shown in FIG. 1B. The image has also beenpartitioned into block areas with constant sizes for a localconsideration of the image. Accordingly, the color characteristic ofeach block areas is represented by the local color histogram as shown inFIG. 1D.

If only the global color histogram as shown in FIG. 1B is utilized inthe image search, local contents of the image would not be considered.However, if the local color histogram as shown in FIG. 1D is alsoutilized to take into consideration the local contents, a memory with alarge storage capacity would be required for storing each local colorhistogram data. Also, it would be difficult to easily represent thecolor characteristic of the image.

Numerous search algorithms according to the image characteristics havebeen conventionally utilized in the content base image searching system.The search algorithm for the image searching system is typically basedupon a local representative color, a major color region (MCR), and aglobal color histogram.

The local representative color indicates a representative color valuefor each grid of a divided image. If there is no color representable ina grid, a “Don't Care” symbol would be represented. The major colorregion indicates a position on which a major color of the image isrepresented. For example, the major color grid may be represented as aminimum square grid. The major color here means a color which has beendistributed more than a specific threshold.

As discussed above, in the content base image searching system of therelated art, numerous algorithm may be used in considering severaldifferent search characteristics. Moreover, in searching images, a usermanually controls the weight of the algorithm through a user interface.However, it is very difficult for a user to directly control the imagesearching algorithm during an image search.

Especially, there may be a significant differences in visual angle orsensual level of a person resulting in different contents stored in thedatabase. Furthermore, for a user who is not a highly trained expertgroup, manually controlling the weight of numerous search algorithm isvery difficult.

Finally, the differences in the visual angle or sensual level of personsmakes it difficult to determine the weight of the optimum searchalgorithm while maintaining a high speed image searching system.

SUMMARY OF THE INVENTION

Accordingly, an object of the present invention is to solve at least theproblems and disadvantages of the related art.

An object of the present invention is to provide a method fordesignating a local representative color value on a color image.

Another object of the present invention is to provide a method for anautomatic selection of the most proper search algorithm in an imagesearching system.

Still another object of the present invention is to provide a method foran automatic selection of a search algorithm in a content base imagesearching system utilizing a characteristic weight.

Additional object of the present invention is to provide a method forauto-determining the detection algorithm in an image detector capable offurnishing linearly the weight according to the number of color blocksof the local grid.

Additional advantages, objects, and features of the invention will beset forth in part in the description which follows and in part willbecome apparent to those having ordinary skill in the art uponexamination of the following or may be learned from practice of theinvention. The objects and advantages of the invention may be realizedand attained as particularly pointed out in the appended claims.

To achieve the objects and in accordance with the purposes of theinvention, as embodied and broadly described herein, a method fordesignating a local representative color value on a color image includesthe steps of: dividing a color image area into blocks of a constantsize; building color histograms for each blocks and comparing themaximum value of the histogram with a first reference value; designatingthe maximum value as a representative color value for its block if themaximum value is more than the first reference value; obtaining colorgroups from the color histograms of respective blocks and comparing thegained values to a second reference value if the maximum value is lessthan the first reference value; and designating a maximum color groupvalue as a representative color value of its block if the maximum colorgroup value is more than the second reference value; and if the maximumcolor group value is less than the second reference value, attaining adistribution rate of the color group from the color histogram of eachblock, distributing and adjusting a mean or an appropriate weight ofvalues more than a given threshold in the distribution rate, from theattained values, and designating the value as the representative colorvalue of the corresponding block.

A method for automatically determining the searching algorithm selectorin an image searching system comprises the steps of: searching thenumber of color blocks CB having color information of the blocks in areference image; comparing the number of the color blocks with adetermined reference value and determining weights to be assigned to atleast two search algorithms; and performing a comparison search of animage by the search algorithm based on the determined weight.

BRIEF DESCRIPTION OF THE ATTACHED DRAWINGS

The invention will be described in detail with reference to thefollowing drawings in which like reference numerals refer to likeelements wherein:

FIGS. 1A to 1E are diagrams providing a method for designating imagecolor values;

FIGS. 2A-2C are flow charts illustrating methods for designating localrepresentative color values on a color image according to the presentinvention;

FIG. 3 is a block diagram showing an image searching system according tothe present invention;

FIG. 4 is a flow chart showing a method for selecting an optimalsearching algorithm according to one embodiment of the presentinvention; and

FIG. 5 is a flow chart showing a method for selecting an optimalsearching algorithm according to a second embodiment of the presentinvention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENT

Reference will now be made in detail to the preferred embodiments of thepresent invention, examples of which are illustrated in the accompanyingdrawings.

FIGS. 2A-2C are flow charts illustrating a method for designating alocal representative color value on a color image. Referring to FIG. 2A,in step S10, a color histogram is built for each block and a maximumvalue of the histogram is designated as a representative color value ofits block.

Particularly, the local color histograms for each block of the image asshown in FIG. 1E is built in step S1, and the maximum color value fromeach local color histograms is selected in step S12. The selectedmaximum color value Max_C is compared with a predetermined referencevalue R_(M) in step S13. If the maximum color value Max_C is greaterthan the predetermined reference value R_(M) in step S13, the value isdesignated as the maximum color value in step S14 and also as therepresentative color value of the corresponding block in step S52.

If the selected maximum color value Max_C is less than the referencevalue R_(M) in step S13, the corresponding block is initially designatedas a “don't care” region in step S15. In step S53, a determination ismade whether the representative color value designation is completed forthe overall image, i.e. whether a representative color value has beendesignated for the last block of the image, and repeating the stepsS11-S14, and S52 until the representative color value is designated forthe last block.

To reduce the number of blocks designated as a “don't care” region, stepS20 may be added to the method of designating the representatve colorvalues. Referring to FIG. 2B, in step S20, a hue group is furtherextracted from the color histograms of the blocks, and the maximum valuefrom the extracted values is designated as a representative color valuefor the corresponding block.

If the selected maximum color value Max_C is less than the referencevalue R_(M) in step S13, a determination is made whether a similar coloris within an adjacent block. If a local color histogram of a block doesnot contain a color greater than a given threshold R_(M), the block mayhave a color distribution in which one specific color cannot bedominantly perceived. Accordingly, a consideration is made whether thereexists a similar color within the block to thereby designate a moreeffective representative color value visually or sensually.

Particularly, before progressing to step S53, hue group histograms areobtained from the adjacent blocks by mapping similar color groups instep S21, for example, a color group of a red, a color group of a blue,etc. The hue group histograms may be derived from the already existinghistograms of the image. In step S22, the maximum color group from thehue group histograms is selected, and the mean value of the maximumcolor group Max_H is compared with a predetermined reference valueR_(MH) in step S23. If the mean hue color group value Max_H is greaterthan the reference value R_(MH), the value is designated as the maximumcolor value in step S24 and also as the local representative color valuein step S52. If the maximum color group value Max_H is less than thereference value R_(MH), the block is again designated as a “don't care”region in step S25.

As an alternative method to reduce the number of blocks designated as a“don't care” region or to further reduce the number of such blocks, stepS30 may be added to the method of designating the representatve colorvalues after either step S20 or S30. Referring to FIG. 2B, in step S30,a distribution rate for the color group is further obtained from thecolor histogram of each block to designate a representative color value.

If the mean color group value Max_H is less than the reference valueR_(MH) in step S23, a local representative hue group value from localhue group histograms is designated for each block in consideration ofthe color distribution in step S31. In step S32, the localrepresentative hue group value Hi is compared with a predeterminedreference value R_(H)<R_(MH), and if Hi is greater than or equal to thereference value R_(H), the distribution rate Vi of the hue group valueHi is compared to a reference value Rv in step S34. If the distributionrate Vi is greater than or equal to the reference value Rv, the huegroup is determined to be evenly distributed. In step S35, a mean valuewithin the evenly distributed color group Hi is extracted and the meancolor corresponding to the mean value is stored in step S36.

In step S37, a determination is made whether the local representativehue group designation is completed for the overall image and stepsS31-S36 are repeated until the local representative hue group isdesignated for the last block. Upon completion, a determination is madein step S38 whether a mean color of the hue group has been stored foreach blocks. If a mean color has been stored, that mean color isregarded as mean color for the corresponding block in a step S51, andthe value is designated as the representative color value in a step S52.However, if there is no mean color of the hue group corresponding to ablock, the block remains designated as a “don't care” region.

Subsequently, the process proceeds to step S53 as discussed above,thereby designating one color value for each block as the representativecolor value as shown in FIG. 1E. The designated local representativecolor values shown in FIG. 1E may be one of the maximum color value of alocal color histogram greater than a first threshold, the maximum huegroup value greater than a second threshold, or the mean color of arepresentative hue group greater than a third threshold.

FIG. 3 is a block diagram of an image searching system according to thepresent invention. Referring to FIG. 3, the image searching systemcomprises an image data input unit 101; an analysis and process unit 102executing an analysis and a processing of images; a plurality ofcomparison search units 103˜105 performing a comparison and search ofthe images based upon weights of image characteristics; an outputdisplay unit 106 outputting and displaying the result of the comparisonand search; a feature extraction unit 107 extracting a feature of theimages; and a characteristic database 108 storing target images andcorresponding characteristic information.

The feature extraction unit 107 includes a global histogram (GH) datamodule 107 a having the color information of an overall image, a localrepresentative color (LRC) data module 107 b having the colorinformation of each blocks, and a major color region (MCR) data module107 c having the color information of a region. The characteristicdatabase 108 also includes a global histogram (GH) data module 108 a, alocal representative color (LRC) data module 108 b, and a major colorregion (MCR) data module 108 c, each data modules storing respectivecharacteristic information of the target images.

FIGS. 4 and 5 are flow charts showing a method for an automaticdetermination of an optimal search algorithm according to the presentinvention.

FIG. 4 shows a first embodiment of a method for an automaticdetermination of an optimal search algorithm including the steps of:inputting an image, detetermining whether the image is a referenceimage, and detecting the number of color blocks CB having colorinformation from the blocks of the reference image (steps S100, S101,S104 and S105); extracting and storing the feature information of imagein respective data modules if the input image is not a reference image(steps S102˜S103); comparing the number of the color blocks with apredetermined first reference value Ref1 and determining weights for theGH and LRC according to the comparison result (steps S106˜S109); andperforming a comparison search of the image by the search algorithmbased on the determined weight and classifying the search result (stepsS111 and S112).

FIG. 5 shows a another embodiment of a method for an automaticdetermination of an optimal search algorithm including the steps of:inputting an image, determining whether the image is a reference image,and detecting the number of color blocks CB having color informationfrom the blocks of the reference image (steps S100, S101, S104 andS105); extracting and storing the feature information of image inrespective data modules if the input reference is not a reference image(steps S102˜S103); comparing the number of the color blocks with aplurality of reference values and determining weights for at least twosearch algorithms according to the comparison result (steps S106˜S110);and performing a comparison search of the image by the search algorithmbased on the determined weight and classifying the search result (stepsS111 and S112).

The operations of the first and second embodiments will be described inreference to FIGS. 3 to 5.

If the image data input to the image data input unit 101 is a referenceimage, a comparison-search is performed, and but if the image is atarget image, the feature extraction unit 107 extracts and stores itsfeature value in the database 108. The target image is a real image andis the image in which a search is made while the reference image is astandard image used for a comparison and is typically a real image butmay also be a virtual image made by a user in a content base imagesearch system.

In the image feature extraction to establish the database, the globalhistogram data GH, the local representative color information LRC andthe major color information MCR are extracted and are storedrespectively in the GH, LRC, and MCR of the database 108. The contentcomparison-search based upon the GH, LRC and MCR is performed by eachcomparison search unit 103 to 105.

Particularly, the analysis and process unit 102 analyzes the image data,and first to third comparison search units 103˜105 execute a searchalgorithm for the analyzed image information. Namely, the comparisonsearch units 103˜105 each execute search algorithms based upon GH, LRCand MCR information from the feature extraction unit 107 and thedatabase 108. In the embodiments of the present invention illustrated,the number of search algorithms has been limited to two or three, andthe comparison searches are based upon GH, LRC, and MCR. However, thepresent invention is not limited to such embodiments.

After the initial analysis and processing of the image information, theimage divided into blocks by an image grid and the number of the colorblocks CB is detected. For example, an image may be divided into gridsof ‘width×height=8×8=64′. Histograms, namely local histograms, for eachrespective grids is built. A color index of a representative color inthe local histogram is designated as the local representative color LRCfor a corresponding grid. However, not every grid would have a LRC andwould be designated as a “don't care” region, as discussed above.

The number of color blocks CBs with LRCs, i.e. not designated as “don'tcare” region, are detected and the detected number of CB indicates acharacteristic of the input image. Subsequently, the detected number ofCBs is utilized in selecting an optimal search algorithm.

If the characteristic of the image is generally distinct in each grid,the detected number of CBs would be high. Because of the distinct gridcharacteristics, a search based upon the LRC would be most effective.Thus, the LRC algorithm is given maximum weight if the detected numberof CBs is high.

If the characteristic of the image is distributed throughout the entireimage, the number of CBs detected would be fairly low. Because thecharacteristic of the image is distributed, a search based upon the GHwould be most effective. Thus, the GH alghorithm is given the maximumweight if the detected number of CBs is low.

However, if the characteristic of the image are clustered and appears inregions of the image, the detected number of CBs would neither be highor low, but somewhere in between depending upon the image. Thus, it isdifficult to decide whether a search based upon the LRC or GH would bemost effective. Accordingly, the MCR algorithm is given the maximumweight if the detected number of CBs is neither high or low.

Particularly, the detected number of CBs is compared to predeterminedreference values Ref1, Ref2 where Ref1<Ref2. If the number of CBs isless than or equal to the first reference value Ref1, GH is assigned themaximum weight. If the number of CBs is greater than Ref1, and thenumber of CBs is more than the second reference value Ref2, the LRC isassigned the maximum weight. If the number of CBs is greater than Ref1,and the number of CBs is less than or equal to Ref2, i.e. the number ofCBs is between Ref1 and Ref2, MCR is assigned the maximum weight.

As shown in FIGS. 4 and 5, the weights assigned to the image searchingalgorithms may a nonlinear type or linear type according to the CBsnumber. Accordingly, different weights are assigned to the threecomparison search algorithms, and the image searching is performed basedupon the weighted search algorithms. The result of the comparison isdisplayed through the output display unit 106, thereby completing theimage search.

The present invention may be easily implemented in any image searchingsystem. Also, the standard for searching the image characteristic toassign the appropriate weights need not be the detection of the numberof CBs. Any other method may be used as the standard for searching theimage characteristic.

In addition, the method for designating the local representative colorvalue allows a consideration of a color distribution of each blockwithout having to unnecessary information. Rather than storing the locala histogram, one color per one block is designated as the representativecolor value and stored.

Furthermore, the present invention also selects the most appropriatesearch algorithm automatically enabling an automatic control of theweights assigned to the search algorithms. As a result, a convenientoperation for the image searching system is available to even generalusers and non-effectiveness of the search system caused by an individualdeviation can be prevented.

The foregoing embodiments are merely exemplary and are not to beconstrued as limiting the present invention. The present teachings canbe readily applied to other types of apparatuses. The description of thepresent invention is intended to be illustrative, and not to limit thescope of the claims. Many alternatives, modifications, and variationswill be apparent to those skilled in the art.

What is claimed is:
 1. A method for designating a local representativecolor (LRC) values of a color image, comprising: (a) dividing a colorimage into a plurality of blocks; (b) building a color histogram foreach block and designating the maximum color value of the colorhistogram as a LRC value of a corresponding block if the maximum colorvalue meets a first condition; and (c) repeating step (b) until the lastblock of the image is processed, wherein if the maximum color value of ablock does not meet the first condition, the method further comprises,building hue group histograms from adjacent blocks of the block,selecting a maximum color group from the hue group histograms anddetermining a representative value of the maximum color group, anddesignating the representative value of the maximum color group as theLRC value of the block if the representative value of the maximum colorgroup meets a second condition.
 2. The method of claim 1, wherein step(b) further comprises: designating the maximum color value as a LRCvalue if the maximum color value is greater than a first referencevalue; otherwise designating the block corresponding to the maximumcolor value as a “don't care” region.
 3. The method of claim 1, whereinthe representative value is a mean value of the maximum color group, andwherein the designating the LRC value comprises: designating the meanvalue of the maximum color group as the LRC value if the mean value ofthe maximum color group is greater than a second reference value;otherwise designating the block as a “don't care” region.
 4. The methodof claim 3, wherein if the mean value of the maximum color group of theblock does not meet the second condition, the method further comprises:building local hue group histograms and determining a localrepresentative hue group value from the local hue group histograms; anddesignating the mean value of the local representative hue group valueas the LRC value if the local representative hue group value meets athird condition and if the distribution of the local representative huegroup value meets a fourth condition.
 5. The method of claim 4, furthercomprising: designating the mean value of the local representative huegroup value as the LRC value if the local representative hue group valueis greater than or equal to a third reference value and if saiddistribution is greater than or equal to a fourth reference value;otherwise designating the block as a the “don't care” region.
 6. Themethod of claim 5, wherein the third reference value is less than thesecond reference value.
 7. The method of claim 1, wherein if the maximumcolor value of the block does not meet the first condition, the methodfurther comprises: building local hue group histograms and determining alocal representative hue group value from the local hue grouphistograms; and designating a representative value of the localrepresentative hue group value as a the LRC value if the localrepresentative hue group value meets a third condition and if thedistribution of the local representative hue group value meets a fourthcondition.
 8. The method of claim 7, further comprising: designating therepresentative value of the local representative hue group value as theLRC value if the local representative hue group value is greater than orequal to a third reference value and if said distribution is greaterthan or equal to a fourth reference value; otherwise designating theblock as a “don't care” region.
 9. The method of claim 1, wherein theplurality of blocks comprise equal blocks, and wherein therepresentative value is an average value of the maximum color group. 10.A method for designating a local representative color (LRC) values of acolor image, comprising: (a) dividing a color image into a plurality ofblocks; (b) building a color histogram for each block and designatingthe maximum color value of the color histogram as a LRC value of acorresponding block if the maximum color value meets a first condition;and (c) repeating step (b) until the last block of the image isprocessed, wherein if the maximum color value of a block does not meetthe first condition, the method further comprises, building local huegroup histograms and determining a local representative hue group valuefrom the local hue group histograms, and designating a representativevalue of the local representative hue group value as the LRC value ifthe local representative hue group value meets a second condition and ifthe distribution of the local representative hue group value meets athird condition.
 11. A method of claim 10, wherein the representativevalue is a mean value of the local representative hue group value,further comprising: designating the mean value of the localrepresentative hue group value as the LRC value if the localrepresentative hue group value is greater than or equal to a secondreference value and if said distribution is greater than or equal to athird reference value; otherwise designating the block as a “don't care”region.
 12. The method of claim 10, wherein the plurality of blockscomprise equal blocks.
 13. A method for designating a localrepresentative color (LRC) values of a color image, comprising: dividinga color image into a plurality of blocks; building a color histogram foreach block; building hue group histograms from adjacent blocks of ablock; selecting a maximum color group from the hue group histograms anddetermining a representative value of the maximum color group;designating the representative value of the maximum color group as a LRCvalue of the block if the representative value of the maximum colorgroup meets a first condition, wherein if the representative value doesnot meet the first condition, the designating the LRC values of thecolor image comprises, building local hue group histograms anddetermining a local representative hue group value from the local huegroup histograms; and designating a representative value of the localrepresentative hue group value as the LRC value if the localrepresentative hue group value meets a second condition and if thedistribution of the local representative hue group value meets a thirdcondition.
 14. The method of claim 13, further comprising: designating amean value of the local representative hue group value as the LRC valueif the local representative hue group value is greater than or equal toa second reference value and if said distribution is greater than orequal to a third referee value; otherwise designating the block as a“don't care” region.
 15. The method of claim 14, wherein the pluralityof blocks comprise equal blocks.
 16. A method for designating a localrepresentative color (LRC) values of a color image, comprising: dividinga color image into a plurality of blocks; determining a color histogramfor each block; and designating a local representative color (LRC) foreach of the plurality of blocks, wherein the designating the LRCcomprises, determining a first criteria of the color histogram as theLRC of a corresponding block when the first criteria meets a firstcondition; determining a second criteria as the LRC of the correspondingblock when the first criteria does not meet the first condition and thesecond criteria meets a second condition, wherein the determining thesecond criteria comprises, building first group histograms from relatedblocks of the corresponding block, selecting the second criteria fromthe first group histograms, and determining the second criteria as theLRC of the corresponding block when the second criteria meets the secondcondition; determining a third criteria as the LRC of the correspondingblock when the second criteria does not meet the second condition andthe third criteria meets a third condition, wherein the determining thethird criteria comprises, building second group histograms, selectingthe third criteria from the second group histograms, and determining thethird criteria as the LRC of the corresponding block when the thirdcriteria meets the third condition; and designating the correspondingblock as a “don't care” region when the first through third criteria donot meet the first through third conditions, respectively.
 17. Themethod of claim 16, wherein the first criteria is a maximum color valueof the color histogram.
 18. The method of claim 16, wherein the secondcriteria is a mean value of a maximum color group from the first grouphistograms, and wherein the first group histograms are local hue grouphistograms.
 19. The method of claim 16, wherein the third criteria is amean value of a local representative hue group value and a distributionof the local representative hue group value meets a fourth condition,and wherein the second group histograms are local hue group histograms.20. The method of claim 16, wherein the first criteria is a color valueof the color histogram that satisfies a first threshold, wherein thesecond criteria is a representative value of a maximum color group fromhue group histograms, wherein the third criteria is a representativevalue of a local representative hue group value from the local hue grouphistograms.